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Hesitant fuzzy multi-attribute decision making based on binary connection number of set pair analysis
Qing Shen1,2, Jungang Lou3,2, Yong Liu4
1School of Science and Engineering, Huzhou College, Huzhou, 313000 Zhejiang China.
Soft Computing
|October 11, 2021
Summary
This study introduces a new model for multi-attribute decision making using hesitant fuzzy sets and binary connection numbers. It effectively ranks alternatives, even with uncertainty, offering versatile and targeted decision support.
Area of Science:
- Decision Science
- Fuzzy Mathematics
- Artificial Intelligence
Background:
- Multi-attribute decision making (MADM) often involves uncertainty.
- Hesitant fuzzy sets (HFS) and probabilistic hesitant fuzzy sets (PHFS) model this uncertainty.
- Existing methods may lack versatility in handling hesitant fuzzy information.
Purpose of the Study:
- To develop a novel MADM model incorporating hesitant fuzziness.
- To apply binary connection number (BCN) theory to HFS and PHFS.
- To objectively evaluate the influence of hesitant fuzziness on alternative ranking.
Main Methods:
- Transforming HFS/PHFS into BCN for analysis.
- Establishing a BCN-based MADM model for hesitant fuzzy environments.
- Utilizing BCN theory to derive hesitant fuzzy centers and ranking suggestions.
Main Results:
- The proposed BCN-based MADM model demonstrates versatility.
- It accurately determines optimal schemes under hesitant fuzzy conditions.
- The model's results are consistent with other decision-making methods.
Conclusions:
- The BCN-based MADM model effectively addresses hesitant fuzziness.
- It provides a versatile framework for ranking alternatives with uncertainty.
- The approach supports targeted decision making based on specific hesitant fuzzy conditions.
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